Market Basket Analysis
Market Basket Analysis
Executive Summary
Introduction
Danielle has asked the team to perform a market basket analysis to help Blackwell’s board of directors better understand the clientele that Electronidex is currently serving and if Electronidex would be an optimal acquisition.
A dataset of transactions has been provided. The dataset contains 9835 transactions and 125 different products over a 30-day period, or about 327 transactions a day. This tells us the retailer is neither large, nor small.
Results and conclussions
After the analysis, we conclude Electronidex’s sales are to be categorized in two forms: retail (B2C) and corporate (B2B). Had we had this information previous to our analysis, it would have saved time in the exploration phase.
Interesting patterns and item relationships found: Retail: >
Would Blackwell benefit in selling Electronidex’s items?
Limitations and observations:
Properties of the dataset: 1. The iMac is the product most bought, in 20% of all transactions. This high number stands out considering the large variety of products, especially being the iMac a pricey product. If this number is representative of all sales throughout the year, then Electronidex is potentially profitable. 2. The mean of items bought per transaction is almost 5. Logically, I would say most people in the real world would buy 1 or 2 items per transactions most frequently, in an eletronics store.
Recommendations
Blackwell should acquire Electronidex If Blackwell does acquire Electronidex, do you have any recommendations for Blackwell? (Ex: cross-selling #items, sale promotions, should they remove items, etc.)
Loading Packages and importing datasets
pacman::p_load(readr, rstudioapi, ggplot2, party, dplyr, arules, arulesViz,
RColorBrewer, readxl, tidyr)
setwd("..")
trans <- read.transactions("./Datasets/ElectronidexTransactions2017.csv", format = "basket",
sep = ",", rm.duplicates = TRUE)distribution of transactions with duplicates:
items
1 2
191 10
Preprocessing
transactions as itemMatrix in sparse format with
9835 rows (elements/itemsets/transactions) and
125 columns (items) and a density of 0.03506172
most frequent items:
iMac HP Laptop CYBERPOWER Gamer Desktop
2519 1909 1809
Apple Earpods Apple MacBook Air (Other)
1715 1530 33622
element (itemset/transaction) length distribution:
sizes
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
2 2163 1647 1294 1021 856 646 540 439 353 247 171 119 77 72
15 16 17 18 19 20 21 22 23 25 26 27 29 30
56 41 26 20 10 10 10 5 3 1 1 3 1 1
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 2.000 3.000 4.383 6.000 30.000
includes extended item information - examples:
labels
1 1TB Portable External Hard Drive
2 2TB Portable External Hard Drive
3 3-Button Mouse
labels
1 1TB Portable External Hard Drive
2 2TB Portable External Hard Drive
3 3-Button Mouse
4 3TB Portable External Hard Drive
5 5TB Desktop Hard Drive
6 Acer Aspire
trans@itemInfo$labels <- labels$ProductType
trans@itemInfo$category <- labels$Category
trans_matrix <- as(trans, "matrix")
trans_df <- as.data.frame(trans_matrix)
# Turning into a sparematrix of 1s and 0s.
for (i in 1:ncol(trans_df)) {
trans_df[, i] <- as.integer(trans_df[, i])
}
# Counting number of items
nitems <- c()
for (i in 1:nrow(trans_df)) {
nitems <- c(nitems, sum(trans_df[i, ]))
}Feature Engineering
trans_df$nitems <- nitems
trans_df$laptops <- trans_df[, which(colnames(trans_df) == "LG Touchscreen Laptop")] +
trans_df[, which(colnames(trans_df) == "Acer Aspire")] + trans_df[, which(colnames(trans_df) ==
"HP Laptop")] + trans_df[, which(colnames(trans_df) == "ASUS Chromebook")] +
trans_df[, which(colnames(trans_df) == "Apple Macbook Pro")] + trans_df[,
which(colnames(trans_df) == "Apple MacBook Air")] + trans_df[, which(colnames(trans_df) ==
"Dell Laptop")] + trans_df[, which(colnames(trans_df) == "Eluktronics Pro Gaming Laptop")] +
trans_df[, which(colnames(trans_df) == "Alienware AW17R4-7345SLV-PUS 17\" Laptop")] +
trans_df[, which(colnames(trans_df) == "HP Notebook Touchscreen Laptop PC")]
trans_df$desktop <- trans_df[, which(colnames(trans_df) == "Lenovo Desktop Computer")] +
trans_df[, which(colnames(trans_df) == "iMac")] + trans_df[, which(colnames(trans_df) ==
"HP Desktop")] + trans_df[, which(colnames(trans_df) == "ASUS Desktop")] +
trans_df[, which(colnames(trans_df) == "Dell Desktop")] + trans_df[, which(colnames(trans_df) ==
"Intel Desktop")] + trans_df[, which(colnames(trans_df) == "Acer Desktop")] +
trans_df[, which(colnames(trans_df) == "CYBERPOWER Gamer Desktop")] + trans_df[,
which(colnames(trans_df) == "Dell 2 Desktop")]
trans_df$tablet <- trans_df[, which(colnames(trans_df) == "iPad")] + trans_df[,
which(colnames(trans_df) == "iPad Pro")] + trans_df[, which(colnames(trans_df) ==
"Fire HD Tablet")] + trans_df[, which(colnames(trans_df) == "Samsung Galaxy Tab")] +
trans_df[, which(colnames(trans_df) == "Kindle")]
trans_df$printer <- trans_df$`Epson Printer` + trans_df$`HP Wireless Printer` +
trans_df$`Canon Office Printer` + trans_df$`Brother Printer` + trans_df$`DYMO Label Manker`
trans_df$nmain <- trans_df$printer + trans_df$laptops + trans_df$desktop + trans_df$tablet
trans_df$ncomp <- trans_df$nitems - trans_df$nmain
trans_df$value <- 10 * trans_df$nmain + trans_df$ncompVisualizations
products <- c("laptops", "desktop","printer","tablet")
for (i in products){
print(ggplot(trans_df, aes_string(x = i)) +
geom_bar(fill = "lightgreen", bins = 100) +
ggtitle(paste("Histogram of",i)))
}for (i in products) {
for (j in products[-1]) {
if (i != j | j != i) {
print(ggplot(trans_df, aes_string(x = i, y = j, color = i)) + geom_jitter() +
ggtitle(paste("Scatterplot of", j, "vs", i)))
}
}
}Splitting dataframe between corporates and retailers
# Filtering. Corporate will be those transactions with more than 2 main
# products and 3 complements.
corporate <- filter(trans_df, nmain >= 2 | ncomp >= 3)
# Cleaning the new columns we created before
corporate <- corporate[, -which(colnames(corporate) %in% c("laptops", "desktop",
"printer", "tablet", "nitems", "nmain", "value", "ncomp"))]
# Filtering. Retailers will be those transactions with less than 2 main
# products and 3 complements.
retailer <- filter(trans_df, nmain <= 1 & ncomp <= 2)
# Cleaning
retailer <- retailer[, -which(colnames(retailer) %in% c("laptops", "desktop",
"printer", "tablet", "nitems", "nmain", "value", "ncomp"))]
# Transforming the dataframe into a transaction objecte
trans_corp <- as(corporate == 1, "transactions")
trans_retail <- as(retailer == 1, "transactions")
# Inserting labels and the level category
trans_corp@itemInfo$labels <- labels$ProductType
trans_corp@itemInfo$category <- labels$Category
trans_retail@itemInfo$labels <- labels$ProductType
trans_retail@itemInfo$category <- labels$CategoryitemFrequencyPlot(trans_corp, topN = 10, type = "relative", col = brewer.pal(8,
"Pastel2"), main = "Corp Relative Item Frequency Plot")itemFrequencyPlot(trans_retail, topN = 10, type = "relative", col = brewer.pal(8,
"Pastel2"), main = "Retail Relative Item Frequency Plot") ## Creating rules via apriori algorithm
Rules for products in corporate transactions
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
0.01 0.1 1 none FALSE TRUE 5 0.01 2
maxlen target ext
10 rules FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 58
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[125 item(s), 5835 transaction(s)] done [0.00s].
sorting and recoding items ... [96 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 done [0.00s].
writing ... [1752 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
set of 1607 rules
set of 1752 rules
rule length distribution (lhs + rhs):sizes
2 3 4
1032 672 48
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000 2.000 2.000 2.438 3.000 4.000
summary of quality measures:
support confidence lift count
Min. :0.01011 Min. :0.02775 Min. :0.6424 Min. : 59.0
1st Qu.:0.01148 1st Qu.:0.12435 1st Qu.:1.0782 1st Qu.: 67.0
Median :0.01422 Median :0.20365 Median :1.2491 Median : 83.0
Mean :0.01827 Mean :0.23648 Mean :1.3030 Mean :106.6
3rd Qu.:0.01971 3rd Qu.:0.32164 3rd Qu.:1.4580 3rd Qu.:115.0
Max. :0.12734 Max. :0.69388 Max. :2.7337 Max. :743.0
mining info:
data ntransactions support confidence
trans_corp 5835 0.01 0.01
lhs rhs support
[1] {HP Laptop} => {iMac} 0.12733505
[2] {iMac} => {HP Laptop} 0.12733505
[3] {Lenovo Desktop Computer} => {iMac} 0.09905741
[4] {iMac} => {Lenovo Desktop Computer} 0.09905741
[5] {CYBERPOWER Gamer Desktop} => {iMac} 0.09562982
[6] {iMac} => {CYBERPOWER Gamer Desktop} 0.09562982
[7] {Dell Desktop} => {iMac} 0.09203085
[8] {iMac} => {Dell Desktop} 0.09203085
[9] {ViewSonic Monitor} => {iMac} 0.08157669
[10] {iMac} => {ViewSonic Monitor} 0.08157669
confidence lift count
[1] 0.4267662 1.132930 743
[2] 0.3380346 1.132930 743
[3] 0.4355690 1.156299 578
[4] 0.2629663 1.156299 578
[5] 0.3798502 1.008383 558
[6] 0.2538672 1.008383 558
[7] 0.4372964 1.160885 537
[8] 0.2443130 1.160885 537
[9] 0.4803229 1.275107 476
[10] 0.2165605 1.275107 476
# Items that have high chances of being bought together
corpro_con <- inspect(head(sort(rules_corpro, by = "confidence"), 10)) lhs rhs support confidence lift count
[1] {Dell Desktop,
Lenovo Desktop Computer,
ViewSonic Monitor} => {iMac} 0.01165381 0.6938776 1.842027 68
[2] {Apple Magic Keyboard,
ASUS Monitor} => {iMac} 0.01148243 0.6767677 1.796606 67
[3] {Acer Aspire,
iMac,
ViewSonic Monitor} => {HP Laptop} 0.01045416 0.6630435 2.222205 61
[4] {Acer Desktop,
HP Laptop,
ViewSonic Monitor} => {iMac} 0.01079692 0.6562500 1.742138 63
[5] {ASUS 2 Monitor,
Dell Desktop} => {iMac} 0.01525278 0.6449275 1.712080 89
[6] {ASUS Monitor,
Lenovo Desktop Computer} => {iMac} 0.01645244 0.6442953 1.710402 96
[7] {Acer Desktop,
ASUS 2 Monitor} => {iMac} 0.01079692 0.6428571 1.706584 63
[8] {ASUS Monitor,
ViewSonic Monitor} => {iMac} 0.01388175 0.6428571 1.706584 81
[9] {ASUS Monitor,
Dell Desktop} => {iMac} 0.01336761 0.6393443 1.697258 78
[10] {Acer Desktop,
iMac,
ViewSonic Monitor} => {HP Laptop} 0.01079692 0.6363636 2.132787 63
lhs rhs
[1] {HP Black & Tri-color Ink} => {ViewSonic Monitor}
[2] {ViewSonic Monitor} => {HP Black & Tri-color Ink}
[3] {Acer Aspire,HP Laptop,iMac} => {ViewSonic Monitor}
[4] {Dell 2 Desktop} => {Apple Magic Keyboard}
[5] {Apple Magic Keyboard} => {Dell 2 Desktop}
[6] {Apple Magic Keyboard,iMac} => {ASUS Monitor}
[7] {Brother Printer} => {iPad Pro}
[8] {iPad Pro} => {Brother Printer}
[9] {HP Laptop,ViewSonic Monitor} => {Computer Game}
[10] {HP Wireless Mouse} => {Epson Printer}
support confidence lift count
[1] 0.01113967 0.46428571 2.733711 65
[2] 0.01113967 0.06559031 2.733711 65
[3] 0.01045416 0.46212121 2.720966 61
[4] 0.01439589 0.28378378 2.679415 84
[5] 0.01439589 0.13592233 2.679415 84
[6] 0.01148243 0.21824104 2.551977 67
[7] 0.01028278 0.20547945 2.529478 60
[8] 0.01028278 0.12658228 2.529478 60
[9] 0.01233933 0.15652174 2.509078 72
[10] 0.01165381 0.18630137 2.470610 68
Rules for categories in corporate transactions
trans_corcat <- aggregate(trans_corp, by = "category")
rules_corcat <- apriori(trans_corcat, parameter = list(supp = 0.05, conf = 0.01,
minlen = 2))Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
0.01 0.1 1 none FALSE TRUE 5 0.05 2
maxlen target ext
10 rules FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 291
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[17 item(s), 5835 transaction(s)] done [0.00s].
sorting and recoding items ... [17 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 4 5 done [0.00s].
writing ... [502 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
set of 479 rules
set of 502 rules
rule length distribution (lhs + rhs):sizes
2 3 4 5
136 228 128 10
Min. 1st Qu. Median Mean 3rd Qu. Max.
2.000 2.000 3.000 3.024 4.000 5.000
summary of quality measures:
support confidence lift count
Min. :0.05004 Min. :0.0763 Min. :0.9674 Min. : 292.0
1st Qu.:0.05947 1st Qu.:0.3226 1st Qu.:1.0542 1st Qu.: 347.0
Median :0.07232 Median :0.4639 Median :1.1448 Median : 422.0
Mean :0.09491 Mean :0.5123 Mean :1.1463 Mean : 553.8
3rd Qu.:0.10651 3rd Qu.:0.7248 3rd Qu.:1.2188 3rd Qu.: 621.5
Max. :0.54070 Max. :0.8735 Max. :1.5310 Max. :3155.0
mining info:
data ntransactions support confidence
trans_corcat 5835 0.05 0.01
lhs rhs support confidence lift count
[1] {Laptops} => {Desktop} 0.5407027 0.7879620 1.008059 3155
[2] {Desktop} => {Laptops} 0.5407027 0.6917343 1.008059 3155
[3] {Monitors} => {Desktop} 0.4327335 0.7868495 1.006636 2525
[4] {Desktop} => {Monitors} 0.4327335 0.5536067 1.006636 2525
[5] {Monitors} => {Laptops} 0.3797772 0.6905578 1.006345 2216
[6] {Laptops} => {Monitors} 0.3797772 0.5534466 1.006345 2216
[7] {Laptops,Monitors} => {Desktop} 0.3076264 0.8100181 1.036276 1795
[8] {Desktop,Monitors} => {Laptops} 0.3076264 0.7108911 1.035976 1795
[9] {Desktop,Laptops} => {Monitors} 0.3076264 0.5689382 1.034514 1795
[10] {Computer Mice} => {Desktop} 0.2904884 0.7821874 1.000672 1695
# Items that have high chances of being bought together
corcat_con <- inspect(head(sort(rules_corcat, by = "confidence"), 10)) lhs rhs support confidence lift count
[1] {Computer Mice,
Keyboard,
Laptops,
Monitors} => {Desktop} 0.06272494 0.8735084 1.117501 366
[2] {Accessories,
Keyboard,
Monitors} => {Desktop} 0.05895458 0.8600000 1.100219 344
[3] {Keyboard,
Laptops,
Monitors} => {Desktop} 0.12613539 0.8578089 1.097416 736
[4] {Accessories,
Keyboard,
Laptops} => {Desktop} 0.06152528 0.8568019 1.096128 359
[5] {Computer Mice,
Computer Tablets} => {Desktop} 0.05604113 0.8560209 1.095129 327
[6] {Computer Mice,
Keyboard,
Monitors} => {Desktop} 0.07866324 0.8531599 1.091468 459
[7] {Accessories,
Laptops,
Monitors} => {Desktop} 0.09340189 0.8475894 1.084342 545
[8] {Computer Cords,
Laptops,
Monitors} => {Desktop} 0.05621251 0.8475452 1.084286 328
[9] {Accessories,
Computer Mice,
Monitors} => {Desktop} 0.05724079 0.8455696 1.081758 334
[10] {Computer Mice,
Keyboard,
Laptops} => {Desktop} 0.09048843 0.8448000 1.080774 528
lhs rhs
[1] {Keyboard,Laptops,Monitors} => {Accessories}
[2] {Desktop,Keyboard,Monitors} => {Accessories}
[3] {Keyboard,Monitors} => {Accessories}
[4] {Computer Mice,Laptops,Monitors} => {Accessories}
[5] {Computer Cords,Desktop} => {Keyboard}
[6] {Desktop,Keyboard,Laptops} => {Accessories}
[7] {Computer Mice,Desktop,Monitors} => {Accessories}
[8] {Accessories,Desktop,Monitors} => {Keyboard}
[9] {Computer Cords} => {Mouse and Keyboard Combo}
[10] {Mouse and Keyboard Combo} => {Computer Cords}
support confidence lift count
[1] 0.05158526 0.3508159 1.531047 301
[2] 0.05895458 0.3412698 1.489386 344
[3] 0.06855184 0.3276003 1.429729 400
[4] 0.05347044 0.3260188 1.422827 312
[5] 0.05141388 0.4807692 1.398449 300
[6] 0.06152528 0.3182624 1.388976 359
[7] 0.05724079 0.3177926 1.386926 334
[8] 0.05895458 0.4744828 1.380163 344
[9] 0.05107112 0.3692689 1.364588 298
[10] 0.05107112 0.1887270 1.364588 298
Rules for products in retailers transactions
Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
0.01 0.1 1 none FALSE TRUE 5 0.005 2
maxlen target ext
10 rules FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 20
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[125 item(s), 4000 transaction(s)] done [0.00s].
sorting and recoding items ... [68 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 done [0.00s].
writing ... [22 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
set of 22 rules
set of 22 rules
rule length distribution (lhs + rhs):sizes
2
22
Min. 1st Qu. Median Mean 3rd Qu. Max.
2 2 2 2 2 2
summary of quality measures:
support confidence lift count
Min. :0.005000 Min. :0.04600 Min. :0.5283 Min. :20.00
1st Qu.:0.006375 1st Qu.:0.06696 1st Qu.:1.3676 1st Qu.:25.50
Median :0.007250 Median :0.10666 Median :1.4819 Median :29.00
Mean :0.007932 Mean :0.13853 Mean :1.8501 Mean :31.73
3rd Qu.:0.009062 3rd Qu.:0.19223 3rd Qu.:2.4736 3rd Qu.:36.25
Max. :0.014250 Max. :0.45763 Max. :3.6096 Max. :57.00
mining info:
data ntransactions support confidence
trans_retail 4000 0.005 0.01
lhs rhs support
[1] {CYBERPOWER Gamer Desktop} => {Apple Earpods} 0.01425
[2] {Apple Earpods} => {CYBERPOWER Gamer Desktop} 0.01425
[3] {Apple MacBook Air} => {Apple Earpods} 0.00975
[4] {Apple Earpods} => {Apple MacBook Air} 0.00975
[5] {3-Button Mouse} => {Apple Earpods} 0.00925
[6] {Apple Earpods} => {3-Button Mouse} 0.00925
[7] {Samsung Monitor} => {CYBERPOWER Gamer Desktop} 0.00850
[8] {CYBERPOWER Gamer Desktop} => {Samsung Monitor} 0.00850
[9] {Apple Macbook Pro} => {Apple Earpods} 0.00800
[10] {Apple Earpods} => {Apple Macbook Pro} 0.00800
confidence lift count
[1] 0.16764706 1.3331774 57
[2] 0.11332008 1.3331774 57
[3] 0.06643952 0.5283461 39
[4] 0.07753479 0.5283461 39
[5] 0.20000000 1.5904573 37
[6] 0.07355865 1.5904573 37
[7] 0.23129252 2.7210884 34
[8] 0.10000000 2.7210884 34
[9] 0.21768707 1.7311099 32
[10] 0.06361829 1.7311099 32
# Items that have high chances of being bought together
retpro_con <- inspect(head(sort(rules_retpro, by = "confidence"), 10)) lhs rhs support confidence lift count
[1] {iPhone Charger Cable} => {Apple MacBook Air} 0.00675 0.4576271 3.118413 27
[2] {Acer Monitor} => {CYBERPOWER Gamer Desktop} 0.00675 0.3068182 3.609626 27
[3] {Samsung Monitor} => {CYBERPOWER Gamer Desktop} 0.00850 0.2312925 2.721088 34
[4] {Apple Macbook Pro} => {Apple Earpods} 0.00800 0.2176871 1.731110 32
[5] {Microsoft Wireless Desktop Keyboard and Mouse} => {Apple MacBook Air} 0.00725 0.2132353 1.453051 29
[6] {3-Button Mouse} => {Apple Earpods} 0.00925 0.2000000 1.590457 37
[7] {Dell Laptop} => {Apple Earpods} 0.00625 0.1689189 1.343292 25
[8] {CYBERPOWER Gamer Desktop} => {Apple Earpods} 0.01425 0.1676471 1.333177 57
[9] {3-Button Mouse} => {iMac} 0.00550 0.1189189 1.481856 22
[10] {Backlit LED Gaming Keyboard} => {iMac} 0.00500 0.1156069 1.440585 20
lhs rhs support
[1] {Acer Monitor} => {CYBERPOWER Gamer Desktop} 0.00675
[2] {CYBERPOWER Gamer Desktop} => {Acer Monitor} 0.00675
[3] {iPhone Charger Cable} => {Apple MacBook Air} 0.00675
[4] {Apple MacBook Air} => {iPhone Charger Cable} 0.00675
[5] {Samsung Monitor} => {CYBERPOWER Gamer Desktop} 0.00850
[6] {CYBERPOWER Gamer Desktop} => {Samsung Monitor} 0.00850
[7] {Apple Macbook Pro} => {Apple Earpods} 0.00800
[8] {Apple Earpods} => {Apple Macbook Pro} 0.00800
[9] {3-Button Mouse} => {Apple Earpods} 0.00925
[10] {Apple Earpods} => {3-Button Mouse} 0.00925
confidence lift count
[1] 0.30681818 3.609626 27
[2] 0.07941176 3.609626 27
[3] 0.45762712 3.118413 27
[4] 0.04599659 3.118413 27
[5] 0.23129252 2.721088 34
[6] 0.10000000 2.721088 34
[7] 0.21768707 1.731110 32
[8] 0.06361829 1.731110 32
[9] 0.20000000 1.590457 37
[10] 0.07355865 1.590457 37
Rules for categories in retailers transactions
trans_retcat <- aggregate(trans_retail, by = "category")
rules_retcat <- apriori(trans_retcat, parameter = list(supp = 0.01, conf = 0.01,
minlen = 2))Apriori
Parameter specification:
confidence minval smax arem aval originalSupport maxtime support minlen
0.01 0.1 1 none FALSE TRUE 5 0.01 2
maxlen target ext
10 rules FALSE
Algorithmic control:
filter tree heap memopt load sort verbose
0.1 TRUE TRUE FALSE TRUE 2 TRUE
Absolute minimum support count: 40
set item appearances ...[0 item(s)] done [0.00s].
set transactions ...[17 item(s), 4000 transaction(s)] done [0.00s].
sorting and recoding items ... [17 item(s)] done [0.00s].
creating transaction tree ... done [0.00s].
checking subsets of size 1 2 3 done [0.00s].
writing ... [40 rule(s)] done [0.00s].
creating S4 object ... done [0.00s].
set of 22 rules
set of 40 rules
rule length distribution (lhs + rhs):sizes
2
40
Min. 1st Qu. Median Mean 3rd Qu. Max.
2 2 2 2 2 2
summary of quality measures:
support confidence lift count
Min. :0.01025 Min. :0.03275 Min. :0.5645 Min. : 41
1st Qu.:0.01250 1st Qu.:0.08498 1st Qu.:0.6138 1st Qu.: 50
Median :0.01675 Median :0.12546 Median :0.7535 Median : 67
Mean :0.02275 Mean :0.15262 Mean :0.8038 Mean : 91
3rd Qu.:0.03625 3rd Qu.:0.21769 3rd Qu.:0.9706 3rd Qu.:145
Max. :0.05375 Max. :0.35602 Max. :1.2459 Max. :215
mining info:
data ntransactions support confidence
trans_retcat 4000 0.01 0.01
lhs rhs support confidence
[1] {Monitors} => {Desktop} 0.05375 0.3272451
[2] {Desktop} => {Monitors} 0.05375 0.1881015
[3] {Computer Mice} => {Desktop} 0.03825 0.2875940
[4] {Desktop} => {Computer Mice} 0.03825 0.1338583
[5] {Monitors} => {Laptops} 0.03725 0.2267884
[6] {Laptops} => {Monitors} 0.03725 0.1190096
[7] {Keyboard} => {Desktop} 0.03625 0.2859961
[8] {Desktop} => {Keyboard} 0.03625 0.1268591
[9] {Active Headphones} => {Laptops} 0.03625 0.2449324
[10] {Laptops} => {Active Headphones} 0.03625 0.1158147
lift count
[1] 1.1452145 215
[2] 1.1452145 215
[3] 1.0064531 153
[4] 1.0064531 153
[5] 0.7245637 149
[6] 0.7245637 149
[7] 1.0008611 145
[8] 1.0008611 145
[9] 0.7825317 145
[10] 0.7825317 145
# Items that have high chances of being bought together
retcat_con <- inspect(head(sort(rules_retcat, by = "confidence"), 10)) lhs rhs support confidence lift
[1] {Accessories} => {Desktop} 0.01700 0.3560209 1.2459176
[2] {Monitors} => {Desktop} 0.05375 0.3272451 1.1452145
[3] {Mouse and Keyboard Combo} => {Laptops} 0.02275 0.3204225 1.0237142
[4] {Computer Cords} => {Laptops} 0.01150 0.3006536 0.9605546
[5] {Computer Mice} => {Desktop} 0.03825 0.2875940 1.0064531
[6] {Keyboard} => {Desktop} 0.03625 0.2859961 1.0008611
[7] {Computer Headphones} => {Desktop} 0.01250 0.2577320 0.9019491
[8] {Active Headphones} => {Laptops} 0.03625 0.2449324 0.7825317
[9] {Active Headphones} => {Desktop} 0.03625 0.2449324 0.8571564
[10] {Monitors} => {Laptops} 0.03725 0.2267884 0.7245637
count
[1] 68
[2] 215
[3] 91
[4] 46
[5] 153
[6] 145
[7] 50
[8] 145
[9] 145
[10] 149
lhs rhs support
[1] {Accessories} => {Desktop} 0.01700
[2] {Desktop} => {Accessories} 0.01700
[3] {Desktop} => {Monitors} 0.05375
[4] {Monitors} => {Desktop} 0.05375
[5] {Mouse and Keyboard Combo} => {Laptops} 0.02275
[6] {Laptops} => {Mouse and Keyboard Combo} 0.02275
[7] {Computer Mice} => {Desktop} 0.03825
[8] {Desktop} => {Computer Mice} 0.03825
[9] {Keyboard} => {Desktop} 0.03625
[10] {Desktop} => {Keyboard} 0.03625
confidence lift count
[1] 0.35602094 1.245918 68
[2] 0.05949256 1.245918 68
[3] 0.18810149 1.145215 215
[4] 0.32724505 1.145215 215
[5] 0.32042254 1.023714 91
[6] 0.07268371 1.023714 91
[7] 0.28759398 1.006453 153
[8] 0.13385827 1.006453 153
[9] 0.28599606 1.000861 145
[10] 0.12685914 1.000861 145
Rules visualizations
methods <- c("graph", "scatterplot")
for (i in methods) {
plot(rules_corpro, method = i, control = list(type = "items"), max = 10)
plot(rules_corcat, method = i, control = list(type = "items"), max = 10)
plot(rules_retpro, method = i, control = list(type = "items"), max = 10)
plot(rules_retcat, method = i, control = list(type = "items"), max = 10)
}Available control parameters (with default values):
main = Graph for 10 rules
nodeColors = c("#66CC6680", "#9999CC80")
nodeCol = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha = 0.5
cex = 1
itemLabels = TRUE
labelCol = #000000B3
measureLabels = FALSE
precision = 3
layout = NULL
layoutParams = list()
arrowSize = 0.5
engine = igraph
plot = TRUE
plot_options = list()
max = 100
verbose = FALSE
Available control parameters (with default values):
main = Graph for 10 rules
nodeColors = c("#66CC6680", "#9999CC80")
nodeCol = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha = 0.5
cex = 1
itemLabels = TRUE
labelCol = #000000B3
measureLabels = FALSE
precision = 3
layout = NULL
layoutParams = list()
arrowSize = 0.5
engine = igraph
plot = TRUE
plot_options = list()
max = 100
verbose = FALSE
Available control parameters (with default values):
main = Graph for 10 rules
nodeColors = c("#66CC6680", "#9999CC80")
nodeCol = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha = 0.5
cex = 1
itemLabels = TRUE
labelCol = #000000B3
measureLabels = FALSE
precision = 3
layout = NULL
layoutParams = list()
arrowSize = 0.5
engine = igraph
plot = TRUE
plot_options = list()
max = 100
verbose = FALSE
Available control parameters (with default values):
main = Graph for 10 rules
nodeColors = c("#66CC6680", "#9999CC80")
nodeCol = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
edgeCol = c("#474747FF", "#494949FF", "#4B4B4BFF", "#4D4D4DFF", "#4F4F4FFF", "#515151FF", "#535353FF", "#555555FF", "#575757FF", "#595959FF", "#5B5B5BFF", "#5E5E5EFF", "#606060FF", "#626262FF", "#646464FF", "#666666FF", "#686868FF", "#6A6A6AFF", "#6C6C6CFF", "#6E6E6EFF", "#707070FF", "#727272FF", "#747474FF", "#767676FF", "#787878FF", "#7A7A7AFF", "#7C7C7CFF", "#7E7E7EFF", "#808080FF", "#828282FF", "#848484FF", "#868686FF", "#888888FF", "#8A8A8AFF", "#8C8C8CFF", "#8D8D8DFF", "#8F8F8FFF", "#919191FF", "#939393FF", "#959595FF", "#979797FF", "#999999FF", "#9A9A9AFF", "#9C9C9CFF", "#9E9E9EFF", "#A0A0A0FF", "#A2A2A2FF", "#A3A3A3FF", "#A5A5A5FF", "#A7A7A7FF", "#A9A9A9FF", "#AAAAAAFF", "#ACACACFF", "#AEAEAEFF", "#AFAFAFFF", "#B1B1B1FF", "#B3B3B3FF", "#B4B4B4FF", "#B6B6B6FF", "#B7B7B7FF", "#B9B9B9FF", "#BBBBBBFF", "#BCBCBCFF", "#BEBEBEFF", "#BFBFBFFF", "#C1C1C1FF", "#C2C2C2FF", "#C3C3C4FF", "#C5C5C5FF", "#C6C6C6FF", "#C8C8C8FF", "#C9C9C9FF", "#CACACAFF", "#CCCCCCFF", "#CDCDCDFF", "#CECECEFF", "#CFCFCFFF", "#D1D1D1FF", "#D2D2D2FF", "#D3D3D3FF", "#D4D4D4FF", "#D5D5D5FF", "#D6D6D6FF", "#D7D7D7FF", "#D8D8D8FF", "#D9D9D9FF", "#DADADAFF", "#DBDBDBFF", "#DCDCDCFF", "#DDDDDDFF", "#DEDEDEFF", "#DEDEDEFF", "#DFDFDFFF", "#E0E0E0FF", "#E0E0E0FF", "#E1E1E1FF", "#E1E1E1FF", "#E2E2E2FF", "#E2E2E2FF", "#E2E2E2FF")
alpha = 0.5
cex = 1
itemLabels = TRUE
labelCol = #000000B3
measureLabels = FALSE
precision = 3
layout = NULL
layoutParams = list()
arrowSize = 0.5
engine = igraph
plot = TRUE
plot_options = list()
max = 100
verbose = FALSE
Available control parameters (with default values):
main = Scatter plot for 1752 rules
engine = default
pch = 19
cex = 0.5
xlim = NULL
ylim = NULL
zlim = NULL
alpha = NULL
col = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage = TRUE
jitter = NA
verbose = FALSE
Available control parameters (with default values):
main = Scatter plot for 502 rules
engine = default
pch = 19
cex = 0.5
xlim = NULL
ylim = NULL
zlim = NULL
alpha = NULL
col = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage = TRUE
jitter = NA
verbose = FALSE
Available control parameters (with default values):
main = Scatter plot for 22 rules
engine = default
pch = 19
cex = 0.5
xlim = NULL
ylim = NULL
zlim = NULL
alpha = NULL
col = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage = TRUE
jitter = NA
verbose = FALSE
Available control parameters (with default values):
main = Scatter plot for 40 rules
engine = default
pch = 19
cex = 0.5
xlim = NULL
ylim = NULL
zlim = NULL
alpha = NULL
col = c("#EE0000FF", "#EE0303FF", "#EE0606FF", "#EE0909FF", "#EE0C0CFF", "#EE0F0FFF", "#EE1212FF", "#EE1515FF", "#EE1818FF", "#EE1B1BFF", "#EE1E1EFF", "#EE2222FF", "#EE2525FF", "#EE2828FF", "#EE2B2BFF", "#EE2E2EFF", "#EE3131FF", "#EE3434FF", "#EE3737FF", "#EE3A3AFF", "#EE3D3DFF", "#EE4040FF", "#EE4444FF", "#EE4747FF", "#EE4A4AFF", "#EE4D4DFF", "#EE5050FF", "#EE5353FF", "#EE5656FF", "#EE5959FF", "#EE5C5CFF", "#EE5F5FFF", "#EE6262FF", "#EE6666FF", "#EE6969FF", "#EE6C6CFF", "#EE6F6FFF", "#EE7272FF", "#EE7575FF", "#EE7878FF", "#EE7B7BFF", "#EE7E7EFF", "#EE8181FF", "#EE8484FF", "#EE8888FF", "#EE8B8BFF", "#EE8E8EFF", "#EE9191FF", "#EE9494FF", "#EE9797FF", "#EE9999FF", "#EE9B9BFF", "#EE9D9DFF", "#EE9F9FFF", "#EEA0A0FF", "#EEA2A2FF", "#EEA4A4FF", "#EEA5A5FF", "#EEA7A7FF", "#EEA9A9FF", "#EEABABFF", "#EEACACFF", "#EEAEAEFF", "#EEB0B0FF", "#EEB1B1FF", "#EEB3B3FF", "#EEB5B5FF", "#EEB7B7FF", "#EEB8B8FF", "#EEBABAFF", "#EEBCBCFF", "#EEBDBDFF", "#EEBFBFFF", "#EEC1C1FF", "#EEC3C3FF", "#EEC4C4FF", "#EEC6C6FF", "#EEC8C8FF", "#EEC9C9FF", "#EECBCBFF", "#EECDCDFF", "#EECFCFFF", "#EED0D0FF", "#EED2D2FF", "#EED4D4FF", "#EED5D5FF", "#EED7D7FF", "#EED9D9FF", "#EEDBDBFF", "#EEDCDCFF", "#EEDEDEFF", "#EEE0E0FF", "#EEE1E1FF", "#EEE3E3FF", "#EEE5E5FF", "#EEE7E7FF", "#EEE8E8FF", "#EEEAEAFF", "#EEECECFF", "#EEEEEEFF")
newpage = TRUE
jitter = NA
verbose = FALSE